Abstract

Researchers within a community gather in scientific conferences periodically. As a result, the scientific output, reflected in the papers published in conferences, carries valuable knowledge about the underlying community, such as collaboration groups and the history of the evolution of topics. Researchers can use this knowledge to identify i) candidates for collaboration for future projects, ii) active topics of research, and iii) relevant papers in their field of research. However, to obtain this knowledge, researchers would have to collect and analyze data such as titles, authors, and keywords that might be spread across thousands of papers. The size and the number of relations in such data sets can make the analysis hard using tabular representations such a spreadsheet. Instead, visualizations provide users a graphical representation of the attributes and relations of data on which they can reflect. Through visualizations researchers can obtain an overview of a scientific community, analyze patterns of evolution, and identify entities of interesting. We propose ExtendedEggShell (EES), a unified framework for extracting, modeling and visualizing scientific communities. EES enables users to visualize the collaboration network of a community based in node-link dia- grams, and interact with the graphs by posing queries inspired by meaningful keywords in word clouds. We evaluate the performance of EES by analyzing the complete set of 366 papers published in the software visualization community (VISSOFT). We demonstrate the tool via selected usage examples, on which we analyze 1084 papers from the object-oriented, systems, languages and applications community (OOPSLA). We found that visualizing scientific communities as bigraphs using node-link diagrams helps users to better understand the collaboration within these communities.